Forecasting sales using neural networks

Autor(en): Thiesing, FM
Vornberger, O 
Herausgeber: Reusch, B
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods
Erscheinungsdatum: 1997
Herausgeber: SPRINGER-VERLAG BERLIN
Journal: COMPUTATIONAL INTELLIGENCE: THEORY AND APPLICATIONS
LECTURE NOTES IN COMPUTER SCIENCE
Volumen: 1226
Startseite: 321
Seitenende: 328
Zusammenfassung: 
In this paper, neural networks trained with the back-propagation algorithm are applied to predict the future values of time series that consist of the weekly demand on items in a supermarket. The influencing indicators of prices, advertising campaigns and holidays are taken into consideration. The design and implementation of a neural network forecasting system is described that has been installed as a prototype in the headquarters of a German supermarket company to support the management in the process of determining the expected sale figures. The performance of the networks is evaluated by comparing them to two prediction techniques used in the supermarket now. The comparison shows that neural nets outperform the conventional techniques with regard to the prediction quality.
Beschreibung: 
5th Fuzzy Days International Conference on Computational Intelligence, DORTMUND, GERMANY, APR 28-30, 1997
ISBN: 9783540628682
ISSN: 03029743

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